Several prevalence pathologies are characterized by prolonged periods of apparent well being, interspersed (or at times, terminated) by sudden, acute and often life-threatening events, such as epilepsy, heart attack and psychotic attacks. Chronic pharmacological therapy, aimed at preventing such events, may compromise life quality during the interim periods. Ability to automatically predict such events on time, for example minutes to hours before these events occur, would open a way to an automated preventing therapy, administered specifically during the pre-event time periods.
Description of Prior Cardiac Arrhythmias Prediction Systems
Cardiac arrhythmias result from abnormal electrical conduction, with or without pacing by multiple abnormal foci. Arrhythmias may vary in severity from mild, in which case no treatment is require, to those that are catastrophic and life threatening. Most life-threatening cardiac arrhythmias (LTCA) involve ventricular fibrillations. Atrial fibrillations are usually not life threatening.
Cardiac rhythm monitoring is mainly performed to prevent death due to LTCA. However, current technologies provide little more than detection and recognition of LTCA once it has started. This leaves very little intervention time; the rhythm must be drastically terminated within minutes by defibrillation (“cardioversion”), or permanent neurological damage, or even death, may occur.
Currently, only one method is in common use to predict an impending LTCA, namely, the frequency and complexity of premature ventricular complexes (PVCs). Existing studies suggest that this method is unreliable, because, in the majority of patients, changes in frequency or complexity of PVCs are not specific to the periods that precede initiation of LTCA, and, therefore, these changes have only poor and unreliable predictive capability.
Methods in general clinical use include simple heart rate detection and, in some cases, repetitiveness of premature ventricular complexes (PVCs). The heart rate detector is set at high and low thresholds by the operator, and an alarm sounds if these thresholds are exceeded. More advanced instruments also alarm when target thresholds for PVC frequency are exceeded. However, these instruments are rather simple, primitive, inaccurate and ineffective. Currently, there is no system for predicting LTCA, only detection once they are in progress. Moreover, the specificity for detection of significant arrhythmias is poor.
Recent research demonstrated that changes in RR-interval (RRI) series might be a more accurate predictor of imminent LTCA than PVCs. However, the complexity and variability of RR-changes in different patients, and even in the same patient in different periods of monitoring, obscured application of this method for prediction of LTCA. Previous studies were focused on the detection of a single type of changes in the RR-series and did not allow identifying both linear and nonlinear changes. This diminished the accuracy of analysis, and made the results applicable to a small number of patients. Frequency components of the RR-series contain physiological/pathologically important information about the activity of autonomic nervous system, which, in turn, plays a major role in the initiation of LTCA. However, the non-stationarity of the signal and the fact that it is not uniformly sampled affects the accuracy of spectral techniques. To overcome this problem, analysis based on Fast Fourier transform (FFT) or autoregressive modeling is usually employed on short and relatively stationary parts, or segments, of the signal. Another approach uses the wavelet transform to decompose the signal into predefined frequency elements. However, neither method allows reliable identification of the frequency elements that exhibit changes before LTCA. The analysis of short time windows requires stationarity of each portion of the signal, whereas the RR-series exhibits pronounced changes preceding LTCA. The wavelet transform decomposes the signal into constant frequency ranges, while individual RR-signals have highly variable frequency content.
The linear changes before LTCA in the majority of patients (80–90%) are not different from those during the arrhythmia-free periods. Because these changes are not specifically associated with LTCA, in the majority of patients they cannot be used for the short-term prediction of arrhythmias. Conventional heart rate variability analysis in the frequency domain has revealed a complex pattern of changes but fails to identify specific changes that might predict LTCA as well. Moreover, the standard time (mean and standard deviation) and frequency (power spectrum) domain representations of a signal do not reveal the nonlinear changes that may precede LTCA. Several studies show that utilization of non-linear measures, or features, derived from biomedical signals, significantly enhances the prediction capability, (a reference to an exemplary study may be made to “A reduction in the correlation dimension of heartbeat intervals precedes imminent ventricular fibrillation in human subjects.”, J. E. Skinner, C. M. Pratt and T. Vybiral, Am. Heart J. vol. 125, pp. 731–743, 1993).
RR-changes are highly variable, with respect to different persons, and even with respect to the same person over different periods of time, state of sleep, emotional state and degree of exertion, all contribute person-specific changes and perturbations to the classical HRV characteristic of the resting, awake and relaxed state.
It should be appreciated that most LTCAs are expected in patients with very sick hearts. Their “baseline” cardiac activity is very pathological and the transition to the LTCA may be obscured. LTCAs are foremost a result of cardiac pathology rather than a pathology of the neural systems which modulate the cardiac rhythm. The latter may however influence arrhythmogenesis by presenting the sick ventricle with a physiological/pathological rhythm variability that occasionally may induce a LTCA. The system may be viewed as a variable signal passing through a variable filter, with some combinations proving disastrous.
Several important conclusions from previous studies:    1) Since single aberrations as well as physiological/pathological RRI variability information may be important for prediction, the use of constant length segments or windows from which to extract features and perform analysis actually defeats the purpose. For RRI information (especially of very low frequencies), one would wish for the longest possible semi-stationary segments, while for ectopies, the shortest may be desirable, or else, the effect of rare ones would be “diluted” by the prevailing rhythms.    2) A single extracted signal feature may not suffice for obtaining all the relevant cardiac and extra-cardiac information pertinent for prediction.    3) It is almost inevitable that any successful prediction system should be trained on records that were known to be pre-LTCA in the patient who's LTCAs it is trying to predict (i.e., the last hour before a serious VT in a patient's 24 h Holter record). Only that way could a true pre-LTCA state be differentiated from the pathological non-LTCA-related background.
U.S. Pat. No. 5,720,294, of Skinner J E, discloses an electrophysiological analyzer. The system disclosed in U.S. Pat. No. 5,720,294 is based on an “improved” point correlation dimension in which the conventional algorithm is tailored to be insensitive to non-stationarities in the signal. According to this system, correlation dimensions are determined for quasi-stationary sub-epochs of the signal and a dimension <1.2 in the RRI signal predicts fatal arrhythmias. However, this system is more adapted for future risk assessment than for predicting an event in an individual patient, who, during his daily routine, may drop the PD2i for other reasons (exertion, emotional stress etc.). Ectopies, although not being pre-filtered from the RRI series, are still considered a “contamination” to be sidestepped by the algorithm rather than assisting the prediction.
U.S. Pat. No. 6,308,094, of Anderson K P et al, discloses a system for prediction of cardiac arrhythmias. The disclosed system utilizes a single signal, i.e. the RR interval (RRI) time series, and derives information from both linear and non-linear variability of this signal. In particular, the time series is divided into time windows of 5 minutes each, and PCA is employed on each time window, and 2–10 KLT coefficients and eigenvectors are derived there from. Time-varying mean and variances of each coefficient are determined, and when more than 4 coefficients simultaneously vary beyond a threshold (i.e. 3 SD), a life-threatening arrhythmia is predicted by 2–4 hours. However, the prediction capability of the system disclosed in U.S. Pat. No. 6,308,094 is rather poor, because the contribution of a single signal to the prediction process, and, thus to the prediction result, is limited.
Description of Prior Epilepsy Prediction Systems
Epileptic seizures of various types affect 2% of the world population. In addition to patients diagnosed as epileptics, normal subjects may be afflicted by acute seizures, as for instance febrile infants and divers using enriched oxygen mixtures. The latter population would particularly benefit from an early (several minutes) alarm of an impending seizure, as onset prevention may be as simple as reducing the level of exertion and/or the diving depth. Also, a reliable early warning in epileptic patients could radically alter current management by substituting chronic drug therapy application with specific measures to suppress a developing seizure. Currently suggested modes of seizure prediction overwhelmingly rely on information gathered from brain electrical activity, i.e. EEG (See, for example U.S. Pat. No. 3,863,625, to Viglione et al.,“Epileptic seizure warning systems”, U.S. Pat. No. 5,720,294, to Skinner J E, “PD2i electrophysiological analyzer”, U.S. Pat. No. 5,857,978, to Hively et al., “Epileptic seizure prediction by nonlinear methods”, WO 00/10455 to Litt et al., “Method and apparatus for predicting the onset of seizures based on features derived from signals indicative of brain activity”, and U.S. Pat. No. 6,304,775 to Iasemidis and Sackellares, “Seizure warning and prediction”.
The brain activity information is very often obtained invasively from sub-dural or intra-cranial electrodes. Most algorithms require multi-channel recordings to predict seizures. The types of seizure so far reportedly amenable to forecasting are focal, complex partial seizures.
Several notions form the rationale of the present invention, which are based on the extensive experience of the applicants. The first is the now widely accepted notion that a seizure is a gradual process in which an ever-growing neuronal mass is recruited into synchronous firing. The second is that the brain is an efficient seizure-quencher and that re many would-be seizures are aborted before reaching the critical synchronized mass. This is the basis for entities known as “Pre-ictal Prodromes” one or more of which often precede the seizure by hours or minutes. It is further noted that individual generalized and also focal seizures progress by varied spatio-temporal routes, depending on the composition of the underlying states of the brain—the variability being between as well as within subjects. This notion would speak against a supervised forecasting approach, based on universal criteria, which currently characterizes all EEG forecasting methods and patent claims. Furthermore, it would predict that any proposed forecasting method is bound to miss some seizures.
However, it should be also noted, that with the advancement of epilepsy, and in particular in focal epilepsy, one or more preferred routes that are prone to lead the progression of seizures may develop, thus reducing the likelihood of quenching and gradually increasing the frequency of seizures. It is the last notion that makes us believe that a simple, non-invasive specific alarm, which would even only abort part of the impending seizures at an early stage of the disease, could have a beneficial effect on its course.
In addition, the present applicant believes that, unlike many other organs, malfunctioning of the brain may be evident in signals emanating in other organs (i.e. the heart). Regarding epilepsy, the premise is that neuronal assemblages of the autonomic-system that affect cardiac rhythm and function may be entrained into the epileptic process at a rather early stage. That they indeed form a part of a fully developed seizure is exemplified in the phenomenon of ictal tachycardia, being a doubling or trebling of the baseline heart rate which coincides with, or even precedes by, several seconds the onset of the EEG electric seizure, as is shown in FIG. 43, which shows exemplary partial complex seizure from a patient with focal temporal epilepsy. FIG. 43A depicts an exemplary ECG and left temporal EEG signals. Seizure is shown by the high amplitude swings on the EEG trace. High amplitude swings on the ECG trace are movement artifacts. FIG. 43B shows RRI series which were extracted from the ECG record shown in FIG. 43A. Ictal tachycardia is evident at the time of the seizure (marked by dotted line), and milder tachicardic episodes precede that seizure.
Several reports (i.e. Sackellares, Iasemidis et al.,“Epilepsy—When Chaos fails” in “Chaos in the Brain” Eds. K. Lehnertz & C. E. Elger, World Scientific, 1999) relying on EEG content complexity measures, have shown alleged seizure-connected changes hours and days before its onset. Even accepting the specific seizure-relatedness of such early changes, the practicality of issuing an alarm and taking measures, particularly pharmacological interventions, at this stage is questioned. One major reason being that once anti-seizure medication is instituted, the state of the brain is changed and the forecasting scheme that was developed to detect the seizures of a particular subject may no longer be valid. The applicant believes that this is a weak point in existing epilepsy forecasting from EEG patents and that a period of 20 minutes before the seizure is a practical period in which to issue an alarm and institute preventive measures.
All of the methods described above have not yet provided satisfactory solutions to the problems of obtaining automatic and reliable prediction of changes of physiological/pathological states and automatic adaptation of the predicting system to an individual patient.
It is an object of the present invention to provide a method for obtaining an automatic and reliable prediction of changes of physiological/pathological states.
It is another object of the present invention to provide a method for automatic and reliable prediction of changes of physiological/pathological states that includes adaptation to an individual patient.
Other objects and advantages of the invention will become apparent as the description proceeds.